Mining & Metals
Mining operations span open pits, underground workings, and processing plants-each running mixed OPC UA, Modbus, and DNP3 equipment in harsh, often remote conditions. KŌJŌ Stack normalizes telemetry at the edge, reduces volume before it leaves the site, and structures every asset into one consistent namespace. Fleet-wide analytics only work when every asset speaks the same structure.
Architecture Highlights
Fleet-Wide Consistency from Pit to Plant
The Problem
Mixed Protocols Across Pit and Plant
Haul trucks, crushers, conveyors, and processing equipment report over OPC UA, Modbus, and DNP3, each with different addressing and update rates. No common structure exists between mobile fleet equipment and fixed plant systems.
Harsh and Remote Conditions Strain Connectivity
Pits and remote sites often rely on constrained wireless or satellite links that were not designed for continuous high-volume telemetry. Equipment that generates the most data is frequently the equipment with the least reliable connection.
High-Volume Telemetry Without Edge Reduction Overwhelms Analytics
Fleet and process sensors generate large volumes of readings, most representing no meaningful change in equipment state. Feeding all of it to central analytics without filtering wastes bandwidth and buries real signal in noise.
What Fails in Traditional Architectures
Without structured, prepared data at the first mile, downstream systems inherit every inconsistency, gap, and limitation of the raw source data.
Fleet-Wide Analytics Require Manual Reconciliation
When haul trucks, crushers, and plant systems each report in their own format, comparing performance across the fleet means normalizing data per equipment type before any analysis begins. Cross-site benchmarking becomes a data engineering project rather than a query.
Remote Sites Lose Data Silently
Without durable buffering, intermittent connectivity at a remote pit means readings generated during a connectivity gap are simply gone. Production and maintenance decisions get made on data that looks complete but is not.
Central Systems Drown in Unfiltered Telemetry
Sending every raw reading from every truck and sensor without edge reduction overwhelms central storage and analytics with values that never meaningfully changed, burying the transitions that actually matter for maintenance and process decisions.
How KŌJŌ Stack Helps
Mixed-Protocol Ingestion Across Pit and Plant
OPC UA, Modbus, and DNP3 equipment-from mobile fleet assets to fixed processing systems-are acquired natively and normalized into a consistent structure at the point of ingestion, regardless of vendor or equipment type.
Edge Normalization and Volume Reduction
Report-by-exception with configurable deadband thresholds filters insignificant changes before data leaves the site. CEL expressions compute derived values and aggregates at the edge, reducing what central systems must process.
Fleet-Wide Consistency via Unified Namespace
Every haul truck, crusher, conveyor, and plant system publishes to the same ISA-95 compliant namespace, so comparing performance across equipment, shifts, or sites does not require reconciling formats first.
Durable Delivery Over Constrained Site Links
Local buffering persists data before acknowledgment, so intermittent wireless or satellite connectivity at remote pits does not translate into lost readings. Buffered data replays in order once connectivity is restored.
Why This Requires First-Mile Data Structuring
Mining operations combine some of the widest protocol diversity in industrial environments with some of the harshest connectivity conditions: mobile haul trucks and shovels report via OPC UA or proprietary fleet management interfaces, fixed crushers and conveyors run Modbus, and remote monitoring points may use DNP3-all while operating in pits and remote sites where wireless and satellite links are constrained and often intermittent. Without normalization at the point of acquisition, building one fleet-wide or pit-to-plant view requires reconciling formats per equipment type, and any connectivity gap at a remote site risks silent data loss. KŌJŌ Stack addresses this by acquiring every protocol natively at the edge, normalizing all of it into the same ISA-95 structure, filtering out non-meaningful readings before they consume scarce bandwidth, and buffering durably so intermittent site connectivity does not translate into missing history. Fleet-wide consistency-from a single haul truck to the entire processing plant-is only achievable when every asset is structured the same way at the point of origin.
Expected Outcomes
Mobile and fixed equipment publish to one canonical namespace
Edge filtering transmits only meaningful equipment-state changes
Buffering and replay maintain continuity across remote-site connectivity gaps
Own the First Mile
Owning the first mile ensures mining & metals data is consistent, contextualized, and usable across the enterprise.